Econ 123

Lecture 1: Introduction

Edward Vytlacil

Yale University

Staff

  • Staff

  • Econometrics vs. Statistics/Data Science

  • Econ 117 & 123 Sequence

  • Econ 123 in Detail

Professor: Edward Vytlacil

  • Economics Ph.d., Univeristy of Chicago,
    • studied under James Heckman.
  • Professor of Economics, Yale University,
    • previously on faculty at Stanford, Columbia, NYU.

Professor: Edward Vytlacil

Professor: Edward Vytlacil

Teaching Fellow: Ken Jung

  • Yale Economics Ph.d. student.

  • Fields:

    • Industrial Organization,
    • Environmental economics.
  • Contact:

Econometrics vs. Statistics/Data Science

  • Staff

  • Econometrics vs. Statistics/Data Science

  • Econ 117 & 123 Sequence

  • Econ 123 in Detail

Course: Econ 123

Second course in Economics dept sequence:
Econometrics and Data Analysis

  • What is econometrics?
    • how is different from probability thoery? statistics?
      data science?
  • Why “and Data Analysis”?

Probability theory

  • Branch of mathematics,
  • Derive implications of known probabilistic model.

Statistics builds upon prob. theory

  • The science of learning from data
    (estimation and inference of probabilistic model),
  • Origins in:
    • math/applied math,
    • experiments,
    • data scarcity.

Data Science

  • If statistics is the “science of learning from data,”
    then what is data science?

  • Is data science another name for applied statistics?

Data Science builds upon stats & CS

  • Data science origins in:
    • CS/engineering (not math),
    • environment of data abundance.
  • Data science overlaps with, but is different from stat:
    • different focus, perspective,
    • different instruction.

Data Science builds upon stats & CS

  • Data science different focus from stat, more focus on:
    • computation,
    • algorithms,
    • data visualization,
    • prediction,
    • domain expertise,
    • work flow…

Data Science builds upon stats & CS

  • Data science instruction different, often
    • less theoretical,
    • more focus on coding,
    • more focus on experience of working with data,
    • inverting standard statistics and econometrics teaching pedagogy.

Econometrics

  • Is econometrics the application of statistics to economic data? of data science to economic data?
  • No!!
    • Does not just apply statistics or data science .. .
    • Does not just consider “economic data.”

Econometrics

Samuelson, Koopmans, and Stone (1954):

the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference

  • For contemporary economics:
    • “economic phenomena” should be broadly construed,
    • econometrics sometimes tightly connected to econ theory, sometimes not.

Econometrics builds upon stats and econ theory

  • Merging of statistics, economic theory, and data
    (and more recently data science)
  • Econometrics origins in:
    • economic models,
    • not in experiments.

Econometrics builds upon stats and econ theory

  • Focus on counterfactual prediction,
    • sometimes answering “why” and often “what if”?
  • Different perspective on models and causality from stats.
    • ceterus paribus paradigm in economics,
    • though some partial convergence across fields.

Goals of econometrics include

  • Understanding economic phenomenon,
    • e.g., why did female labor force participation increase dramatically, especially in professional fields?

Goals of econometrics include

  • Distinguish correlation vs causality,
    • e.g., connection between access to birth control/abortion and female labor force participation?
    • answer “what if” questions.

Goals of econometrics include

Goals of econometrics include

  • Estimate economically meaningful quantities,
    • e.g., supply and demand functions, hedonic equations.
  • Test economic theory, inform economic theory,
    • e.g., distinguish taste-based vs statistical discrimination.

Influence of Econometrics

  • Econometrics grew out of econ, but has been influential in:
    • other social sciences
      (political science, law, …),
    • policy,
    • industry.
  • Has influenced statistics and CS/AI,
    • though relationship often contentious.

Econ 117 & 123 Sequence

  • Staff

  • Econometrics vs. Statistics/Data Science

  • Econ 117 & 123 Sequence

  • Econ 123 in Detail

Econ 117 & 123

Sequence in “Econometrics and Data Analysis’’

  • An Econometrics sequence
    • Considers empirical applications of interest to economists;
    • Teaches how economists think about data, connecting data to economic models…

Econ 117 & 123

Sequence in “Econometrics and Data Analysis”

  • An Econometrics sequence
    • Covers theory and methods from statistics and
      data science, but . . .
    • Focuses on methods developed in econometrics to answer questions of interest to economists,
      • For example, instrumental variables analysis,
        difference in difference methods.

Econ 117 & 123

Sequence in “Econometrics and Data Analysis”

  • Why “and Data Analysis”?
    • inverted teaching pedagogy,
    • more focus on:
      • coding,
      • working with real data,
      • leveraging modern computing power,
      • work flow.

Econ 123 in Detail

  • Staff

  • Econometrics vs. Statistics/Data Science

  • Econ 117 & 123 Sequence

  • Econ 123 in Detail

Goal of Econ 123

  • By the time you complete this course, for you to have the strong basis for conducting original empirical research in
    • economics,
    • other social sciences
    • policy research,
    • industry.

Econ 123 builds upon Econ 117 by:

  • Covering additional, advanced topics including:

    1. computational methods for infernece,
    2. instrumental variables,
    3. panel data methods,
    4. maximum likelihood estimation,
    5. nonlinear models, including limited dependent variable models.

Econ 123 builds upon Econ 117 by:

  • Go deeper on workflow, from acquiring data to presenting results.

  • Building upon the R coding you learned in Econ 117, e.g.,

    • User defined functions,
    • Apply family,
    • Simulation/Monte Carlo analysis.

R in Econ 123

  • In this course, we will use R,
    • in R-Studio IDE, to

R in Econ 123

  • create reproducible documents with R-Markdown/Quarto
    • combining text, code and results.

R in Econ 123

  • In this course, we will use R,

    • what if you want to use STATA, python, . . .
  • Advantages of R over other options? Disadvantages?

Econ 123 Applications include:

  • Finance:
    1. Asset diversification,
    2. Capital Asset Pricing Model,
    3. Event studies.
  • Labor and education economics:
    1. Returns to schooling,
    2. Labor supply
    3. Effect of early childhood interventions.

Econ 123 Applications include:

  • Discrimination, including in
    1. loans,
    2. job market,
    3. police force.

Will relate to economic models of discrimination:
statistical- vs taste-based discrimination.

Econ 123: Prerequisites

  • Pre-Req:
    Econ 117: Introduction to Econometrics and Data Analysis.

  • What if you haven’t taken Econ 117?

    • Econometrics is different from S&DS,
      • no close substitutea for Econ 117 in S&DS,
      • no close substitute for Econ 123 in S&DS.
    • You should take Econ 117!

Econ 123: Prerequisites

  • Pre-Req:
    Econ 117: Introduction to Econometrics and Data Analysis.

  • What if you haven’t taken Econ 117?

    • You should take Econ 117!

    • However, I will permit you to take Econ 123 if:

      • you have taken S&DS 220 or 230, or
      • with special permission.

Classes alternate lectures and labs:

  • Lectures: Lecture slides will be posted on the course webpage, but are not designed as a substitute for attending lecture.

  • Labs: In-class labs where we live-code in R to analyze real data. The labs will be designed to directly help you with your problem sets.

  • You are expected to attend lectures and labs.
    I will call on students.

Course Webpages

Assignments Share of Course Grade
Online Quizzes 10%
Problem Sets 25%
Midterm 20%
Final 25%
Empirical Project 20%

On-Line Quizzes:

  • Posted on Fridays:
    • to the course webpage.
    • most weeks,
    • remain live for 48 hour once posted,
    • you have one hour to complete the quiz once you start it.
  • Focus on theoretical questions, some questions on R coding.

On-Line Quizzes:

  • Open book/open notes,
    • but you cannot collaborate with, or discuss with, other students until the solutions are posted.
  • Lowest quiz score will be dropped.

Problem Sets

  • Primarily empirical, based on academic research papers, though include some theoretical questions.

  • Should be turned in following the same problem set submission guidelines that you used in Econ 117.

  • Due dates are strict.1

  • The lowest problem set score will be dropped.

Problem Sets

  • You may work in groups of up to four students on the problem sets.1

  • However, you must turn in your own assignment and indicate on your submission the other members of the group.

Exams

  • Midterm: Open book/open notes
    • in-class, March 9 (date tentative)
  • Final: Open book/open notes
    • Tuesday, May 9, 2023 at 2pm.
  • focus on theory, with some R related questions including interpreting empirical output from R.

End of Term Empirical Project

  • Address a research question of your choosing, applying the methods from this course to a relevant data set.
  • You may work in groups of up to four students.

End of Term Empirical Project

What’s next

  • Motivating example: labor force participation of women, based on Goldin (2006b)

    • illustrate empirical work in economics, interplay between economic theory, econometrics, and data.
  • Review causal inference, introduce models of discrimination.

  • Review inference, with applications to causal effects and discrimination.

What’s next

What’s next

  • First quiz: Friday January 27
    • covering material from Handouts 1 and 2, and material on inference.
  • First problem set assigned Thursday January 26.

References

Donoho, David. 2017. “50 Years of Data Science.” Journal of Computational and Graphical Statistics 26 (4): 745–66. https://doi.org/10.1080/10618600.2017.1384734.
Goldin, Claudia. 2006a. “The Quiet Revolution That Transformed Women’s Employment, Education, and Family.” National Bureau of Economic Research Cambridge, Mass., USA.
———. 2006b. “The Quiet Revolution That Transformed Women’s Employment, Education, and Family.” American Economic Review 96 (2): 1–21.
Hogg, Robert V, Elliot A Tanis, and Dale L Zimmerman. 2020. Probability and Statistical Inference. 10th ed. Pearson.
Samuelson, Paul A, Tjalling C Koopmans, and J RICHARD N Stone. 1954. “Report of the Evaluative Committee for Econometrica.” JSTOR.
Wooldridge, Jeffrey M. 2020. Introductory Econometrics: A Modern Approach. 7th ed. Cengage Learning.